code: def test_clone_precision_inherition(
      model_clone_precision_inherition, data, backend: str
  ):
      output_dir = str(
          test_root_path / f"hls4mlprj_stream_clone_precision_{backend}"
      )
      layer_config = {
          "inp": {"Precision": "fixed<32,5>"},
          "x": {"Precision": "fixed<32,5>"},
          "x_linear": {"Precision": "fixed<32,5>"},
          "y": {"Precision": "fixed<32,5>"},
          "y_linear": {"Precision": "fixed<32,5>"},
          "out": {"Precision": "fixed<32,5>"},
      }
      hls_config = {
          "Model": {"Precision": "fixed<1,0>", "ReuseFactor": 1},
          "LayerName": layer_config,
      }
      model_hls = convert_from_keras_model(
          model_clone_precision_inherition,
          backend=backend,
          output_dir=output_dir,
          hls_config=hls_config,
          io_type="io_stream",  # clone only happens with stream io.
      )
      assert (
          model_hls.graph["clone_inp"]
          .attributes["inp_cpy1"]
          .type.precision.width
          == 32
      )
      assert (
          model_hls.graph["clone_inp"]
          .attributes["inp_cpy1"]
          .type.precision.integer
          == 5
      )
      assert (
          model_hls.graph["clone_inp"]
          .attributes["inp_cpy2"]
          .type.precision.width
          == 32
      )
      assert (
          model_hls.graph["clone_inp"]
          .attributes["inp_cpy2"]
          .type.precision.integer
          == 5
      )
  
      model_hls.compile()
      r_hls = model_hls.predict(data)
      r_keras = model_clone_precision_inherition(data).numpy()
  
      assert np.allclose(r_hls, r_keras, atol=1e-5, rtol=0)
  
file_path: hls4ml/test/pytest/test_stream_clone.py
imports: from pathlib import Path
  import numpy
  import pytest
  from keras.layers import Add, Dense
  from tensorflow import keras
  from hls4ml.converters import convert_from_keras_model
label_desc: Use image node_id 5 for calling a global function with example usage: test_clone_precision_inherition(model_clone_precision_inherition, data, backend) without return types